1.  Download and install MATLAB. Develop the following function using MATLAB a.  Write a script that loads data from a file and display the first five instances. Create a matrix from the loaded instances. b.  Divide the loaded instances into two sets with one set containing 70% of the instances and the other containing 30% of the instances. Save each set into a variable. Name the variables ‘TrainingSet’ and TestingSet’. c.  Save the two new variable to a ‘.mat’ file. 2.  Implementing Fisher LDA method using MATLAB. a.   Write functions that take the provided data set and compute the optimal projection vector following the fisher criterion. b.  The output from the main function must be the identified projection vector. 3.  Write functions to evaluate the LDA method using MATLAB. a.  Write a script to perform cross-validation and report the average accuracy, standard deviation, sensitivity, and specificity. NOTE: There is no requirement to develop a GUI to visualize the intermediate or the results. A report that includes the following items is due in addition to the source code for the functions: –  A description of each function, –  How to run the functions to get the reported results, and

The assignment involves three main tasks, which can be completed using MATLAB. The first task involves downloading and installing MATLAB and then developing a function using MATLAB. In this function, a script needs to be written to load data from a file and display the first five instances. A matrix needs to be created from these instances.

Next, the loaded instances need to be divided into two sets. One set should contain 70% of the instances and the other set should contain 30% of the instances. These sets should be saved into variables named ‘TrainingSet’ and ‘TestingSet’. Finally, the two new variables should be saved to a ‘.mat’ file.

The second task requires the implementation of the Fisher Linear Discriminant Analysis (LDA) method using MATLAB. To accomplish this, functions need to be written that take the provided dataset and compute the optimal projection vector following the Fisher criterion. The output from the main function should be the identified projection vector.

In the third task, functions need to be written to evaluate the LDA method using MATLAB. A script needs to be written to perform cross-validation and report the average accuracy, standard deviation, sensitivity, and specificity. It is important to note that there is no requirement to develop a graphical user interface (GUI) to visualize the intermediate or the results.

In addition to the source code for the functions, a report needs to be submitted. The report should include a description of each function and how to run the functions to obtain the reported results.

In summary, this assignment requires the use of MATLAB to perform various tasks related to data analysis and evaluation. The tasks include loading and manipulating data, implementing the Fisher LDA method, and evaluating the method using cross-validation. The outputs of these tasks need to be saved, and a report needs to be submitted along with the source code.